Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis

Abstract Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single‐cell data from 92,521 cells to explore the tumour cell landscape. Through clustering analysis, we identified six distinct cell clusters and investigated their differentiation and metabolic heterogeneity using multi‐omics approaches. Notably, cytotrace analysis and pseudotime trajectories revealed distinct stages of tumour cell differentiation, which have implications for patient survival. By leveraging markers from these clusters, we developed a tumour cell‐specific machine learning model (TCM). This model not only predicts patient outcomes and responses to immunotherapy, but also distinguishes between genomically stable and unstable tumours and identifies inflamed (‘hot’) versus non‐inflamed (‘cold’) tumours. Intriguingly, the TCM score showed a strong association with TOMM40, which we experimentally validated as an oncogene promoting tumour proliferation, invasion and migration. Overall, our findings introduce a novel biomarker score that aids in selecting melanoma patients for improved prognoses and targeted immunotherapy, thereby guiding clinical treatment decisions.


| INTRODUC TI ON
Cutaneous melanoma (CM) originates from melanocytes and is a highly malignant form of skin cancer.Over the past several decades, its incidence among Caucasians has increased dramatically, with about 230,000 new cases reported annually worldwide according to the World Health Organization. 1 Immunotherapy has become an indispensable treatment for CM, heralding a new era in the management of this cancer. 2Recent advancements in immunotherapy for CM have produced unprecedented, enduring responses in patients with advanced stages of the disease, unlike conventional chemotherapy.However, these responses are limited to a relatively small group of patients and vary significantly among different individuals with CM.These clinical challenges have driven researchers to identify new tools that can predict which patients are inherently resistant to targeted and immunotherapeutic treatments.Such tools would improve the clinical management of patients and optimize the utilization of clinical resources.
4][5] Advances in transcriptomics and single-cell sequencing technologies have significantly enhanced our understanding of cancer biology, allowing us to observe molecular dynamics within tumours with unprecedented detail. 6anscriptomics enables comprehensive gene expression analysis, aiding in the identification of key regulatory networks that drive melanoma progression and its resistance to treatment. 7This approach allows for detailed characterization of the tumour microenvironment (TME), revealing complex interactions between melanoma cells and the surrounding stroma, immune cells and extracellular matrix. 8ditionally, single-cell sequencing has exposed the inherent heterogeneity of melanoma, identifying distinct cellular subpopulations and their unique transcriptional states. 9This detailed analysis is crucial for understanding the heterogeneity of melanoma epithelial cells.
The TME of melanoma is composed of various cellular constituents such as tumour cells, immune cells, fibroblasts and endothelial cells.Single-cell sequencing has opened unprecedented opportunities for dissecting the complex tumour ecology of melanoma.Recent studies have focused on investigating the immune microenvironment and the heterogeneity within melanoma tumours.Tirosh and colleagues, utilizing single-cell sequencing, identified unique features associated with intrinsic resistance to RAF/MEK inhibitors in CM. 10 Meanwhile, Andrade and his team reported two distinct gene expression programs in natural killer cells that highlight significant functional specialization in melanoma, such as cytotoxicity and chemokine production. 11Sade-Feldman and associates discovered two divergent states of CD8+ T cells that correlate with either regression or progression of patients' tumours, along with a single transcription factor, Tcf7, which predicts positive clinical outcomes in patients undergoing checkpoint therapy in an independent cohort. 12spite these advances, explorations into the heterogeneity of tumour cells in cutaneous melanoma remain limited.Thus, we utilized single-cell sequencing to isolate tumour cells from melanoma samples for re-clustering to further explore their potential heterogeneity.
In this study, we leveraged single-cell data from melanoma to conduct a systematic investigation into the heterogeneity of tumour cells, highlighting variations in their metabolic heterogeneity and differentiation potential.We developed a tumour cell-specific machine learning model (TCM) that is capable of predicting patient outcomes and the efficacy of immunotherapy.This model has been validated across multiple datasets and may assist in the clinical stratification of melanoma patients, facilitating the identification of potential candidates for therapy.

| Dataset source
Sequencing data was retrieved from the GEO database, notably from dataset GSE215120, which comprises 11 samples, including seven acral melanoma and four CM. 13 The Read10X_h5 function was employed to process the h5 file format.In addition, we obtained bulk transcriptome sequencing data from both TCGA and GEO databases, specifically datasets TCGA-SCKM, GSE1456, 14 GSE19234, 15 GSE22153, 16 and GSE65904. 17We utilized the 'sva' package 18 in R to standardize the data and correct for batch effects.For uniformity and comparability across datasets, gene expression data were transformed to the Transcripts Per Million (TPM) format.

| Analysis of single-cell RNA sequencing
The initial gene expression matrix from single-cell RNA sequencing was preprocessed using the Seurat package 19 (version 4.2.0) in R. We set the inclusion criteria for gene expression at a minimum threshold of expression in at least 10 cells.Quality control measures were strictly applied, leading to the removal of cells that either had more than 5000 or fewer than 200 expressed genes, or those that exhibited over 10% of their unique molecular identifiers (UMIs) derived from mitochondrial genes.These stringent criteria refined the single-cell transcriptomic expression matrix.To correct for batch effects, we employed the Harmony R package 20 for data integration.Dimensionality reduction for data visualization was performed using Uniform Manifold Approximation and Projection (UMAP).The identification of differentially expressed genes (DEGs) across various cellular subpopulations was accomplished with the 'FindAllMarkers' function. 21This comprehensive approach ensures robust analysis and interpretation of the complex single-cell RNA sequencing data.

| Investigating cellular dynamics and metabolic functions in tumour development
To analyse developmental trajectories in inferred tumour cells, we utilized the Monocle2 algorithm on a gene-cell matrix based on UMI counts, normalized within a specific subset of Seurat.The creation of a new analysis object was achieved through the 'cell data set' function, which was configured to use the Negative Binomial distribution as the expression family parameter.Cells were subsequently ordered and reduced in dimensionality for trajectory analysis using the default settings of the algorithm.To further explore cellular characteristics, the CytoTRACE package 22 was used to assess the stemness and differentiation potential among different tumour cell subpopulations.Additionally, metabolic pathway activities within various subtypes of tumour epithelial cells were evaluated using the scMetabolism package. 23

| Machine learning-Based discovery of prognostic indicators in SKCM
In this study targeting SKCM, we utilized the GSVA package 24,25 to pinpoint specific tumour clusters within SKCM specimens.The role of critical genes within these clusters was assessed through univariate Cox regression analysis to evaluate their impact on patient survival.Following this initial analysis, a comprehensive validation was conducted using 10-fold cross-validation with a broad spectrum of machine learning models, including stepwise Cox, Lasso, Ridge, plsRcox, CoxBoost, Random Survival Forest (RSF), Generalized Boosted Regression Models (GBM), Elastic Net (Enet), Supervised Principal Components (SuperPC), and Survival Support Vector Machine (survival-SVM).The goal of these analyses was to identify the most significant Prognostic Impact Signature (PIS) for SKCM, distinguished by achieving the highest concordance index (C-index).This methodology ensures a robust assessment of potential prognostic markers in SKCM, leveraging advanced machine learning techniques to enhance the accuracy of predictions regarding patient outcomes.

| Analysing immune cell composition
Seven diverse algorithms for assessing immune cell infiltration-EPIC, TIMMER, CIBERSORT, CIBERSORT-ABS, MCPCounter, QUANTISEQ, and XCELL-were applied to evaluate the immune cell composition.Furthermore, the estimate package 26 was strategically used to calculate immune, stromal and ESTIMATE scores for patients with TCGA-SKCM, facilitating an in-depth analysis of the TME.

| Propagation of human melanoma cell lines WM-115 and A375
The WM-115 and A375, human melanoma cell lines, were sourced from the Cell Resource Center at the Shanghai Life Sciences Institute.

| Gene silencing of TOMM40 in melanoma cell lines
To reduce TOMM40 expression, siRNA targeting TOMM40 (siTOMM40, Table S1) was employed alongside a non-targeting control siRNA (NC siRNA).Cells were seeded in six-well plates at a density that promotes 50% confluency and were transfected with Lipofectamine 3000 (Invitrogen, USA) following the guidelines provided by the manufacturer.

| Colony formation post-TOMM40 knockdown
For colony formation assays, approximately 1 × 10 3 siRNAtransfected WM-115 and A375 cells were cultured per well in sixwell plates for a period of 14 days.After incubation, cells were fixed using 4% paraformaldehyde for 15 min and subsequently stained with Crystal violet (Solarbio, China) following two PBS washes.

| Assessment of cell migration via wound-healing technique
The wound-healing assay was conducted to evaluate the migratory response of the transfected WM-115 and A375 cells.Once achieving 95% confluency in six-well plates, a sterile 20 μL pipette tip was used to create a straight scratch across the cell layer.Cells were then washed twice with PBS to clear detached cells, and wound closure was monitored at 0 and 48 h post-scratch using ImageJ to measure the scratch width.

| Invasion and migration analysis using transwell assay
The Transwell assay was utilized to study the invasive and migratory behaviour of WM-115 and A375 cells following treatment.
Approximately 2 × 10 5 cells were seeded into the upper chamber of 24-well plates, with or without a Matrigel coating, and incubated for 48 h.After incubation, cells that did not invade through the Matrigel were carefully removed from the upper layer, while those that had migrated to the lower surface were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet (Solarbio, China).

| Statistical approache
Statistical analysis, data manipulation, and the generation of graphical outputs were performed using R software, version 4.2.0.The Kaplan-Meier estimator coupled with the log-rank test was employed to assess differences in overall survival (OS) among patient subgroups.Continuous variables were analysed for differences between groups using either the Wilcoxon rank-sum test or the Student's t-test, depending on the data distribution.For categorical variables, the chi-square test or Fisher's exact test was utilized, as appropriate.Corrections for multiple comparisons were made using the false discovery rate (FDR) method.Additionally, Pearson's correlation coefficient was calculated to determine the relationships among various variables.All statistical tests were two-sided, with a significance threshold set at a p-value less than 0.05.

| Dimensional reduction and clustering analysis
Through dimensional reduction clustering, 92,521 cells were classified into eight distinct clusters (Figure 1A).Classic cell-type-specific markers were then used to annotate these clusters (Figure 1B, Figure S1A), eventually identifying five main cell types: tumour cells, NK/T cells, endothelial cells, fibroblasts and myeloid/B cells (Figure 1C).The proportional distribution of these cell types varied across 11 samples (Figure 1D).Subsequently, tumour cells were isolated and re-clustered to identify six tumour cell subgroups, with their proportions across different samples shown in Figure 1F.Cell cycle scoring for these tumour cell subgroups was calculated and visually represented on a UMAP plot using pie charts (Figure S1B), highlighting Cluster3 as a group of proliferative tumour cells.

| Assessment of stemness and metabolic heterogeneity in tumour subclusters
Utilizing CytoTRACE, we evaluated the stemness of different tumour clusters.Results indicated that Cluster3 exhibited greater stemness, characterized by the high expression of the stemness marker STMN1 (Figure 2A), suggesting its strong differentiation potential.Among the six clusters, Cluster0 and Cluster3 had the highest CytoTRACE scores (Figure 2B).The GSVA algorithm was applied to assess the metabolic heterogeneity among the tumour subgroups, with Cluster3 showing significantly increased activity in the purine metabolism pathway (Figure 2C).Examination of 50 hallmark gene sets across the tumour cell clusters revealed that Cluster3 was particularly enriched in cell cycle pathways such as MYC, E2F and G2M (Figure 2D), supporting its identification as a subgroup of earlydifferentiating tumour cells.

| Pseudotime analysis and differential gene expression
Pseudotime analysis revealed a progressive increase in the prevalence of Cluster3, while Cluster1 diminished over time (Figure 3A-D, Figure S2A).A heatmap displayed significant gene expression changes along the pseudotime trajectory, with the top 50 differentially expressed genes highlighted; the top three of these genes are shown in Figure 3E.Further GO enrichment analysis of genes related to pseudotime shows that, in the early stages, there is significant enrichment in the Biological Process category for genes involved in 'regulation of cellular metabolic process', 'regulation of primary metabolic process', and 'regulation of nitrogen compound metabolic process', suggesting a focus on cellular metabolism and biosynthetic processes at this stage.Later in pseudotime, there is notable enrichment such as 'extracellular exosome', 'extracellular vesicle', and 'extracellular region', which may reflect an increased role in cellto-cell communication and signalling as cells progress and differentiate.There is also an enrichment in 'membrane', 'membrane part', and 'organelle', indicating that cellular structures are being more defined and specialized functions are developing (Figure S2B).

| Correlation between tumour cell cluster abundance and survival
Leveraging the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm, we quantitatively assessed the abundance of Clusters 0-5 in TCGA-SKCM samples.Intriguingly, patients with a higher abundance of cluster1 and 3 demonstrated poorer survival outcomes (Figure 4).This suggests a potential prognostic relevance of these clusters in the context of SKCM progression and patient survival.

| Transcriptomic data normalization and prognostic analysis of marker genes in SCKM datasets
Prior to in-depth analysis, batch effect removal was applied to the entire set of transcriptomic data (Figure 5A).Thereafter, the prognostic influence of marker genes within clusters 1 and 3 was assessed in five separate SCKM datasets, from which a subset of 37 genes was selected based on their statistical significance, manifesting p-values less than 0.05 in no fewer than four of the datasets (Figure 5B).
Enrichment analysis, both GO and KEGG, identified pathways in which these genes are notably overrepresented (Figure 5C,D), including but not limited to pathways associated with p53 signalling and cell cycle regulation.Figure 5E delineates the chromosomal copy number aberrations associated with these 37 genes.

| Development of a tumour cell-specific machine learning model
Leveraging the marker genes from tumour cell clusters 1 and 3,

| Enhanced predictive power of the TCM score validated across diverse cohorts
In an expansive assessment of the TCM score's predictive power, we compared it with 95 established prognostic signatures using the concordance index (C-index) metric across seven distinct cohorts.Our findings demonstrated a consistent superiority of the TCM score over the bulk of the existing signatures within all four scrutinized cohorts (Figure 7A-E).Higher TCM scores were significantly associated with adverse outcomes in SKCM, a finding repeatedly validated in four independent datasets: TCGA, GSE1456, GSE119234, GSE22153 and GSE665904.The predictive strength of the TCM score for 1-, 3-, and 5-year overall survival (OS) was evident, with AUC metrics for TCGA (0.6, 0.61, 0.63), GSE1456 (0.76, 0.7), GSE119234 (0.62, 0.78, 0.75), GSE222153 (0.59, 0.67, 0.63), and GSE65904 (0.72, 0.55, 0.54) illustrating its robustness (Figure 8A-E).Principal component analysis (PCA) based on the expression levels of the model's genes distinctly segregated the samples into two clusters within each dataset (Figure 8F-J).

| Immune landscape and correlation with TCM score
To clarify the immunological context reflected by the TCM score, we explored the association between the TCM score and the level of

| Evaluating the prognostic impact of model genes in pan-cancer analysis
We then extended the TCM scores to pan-cancer settings, observing the expression patterns of TCM scores across various cancers (Figure 10A).Through GSEA of the transcriptomes between high and low TCM groups, we investigated the differences in tumour hallmark gene sets.Cell cycle-related pathways, such as G2M_CHECKPOINT, E2F_TARGETS, MYC_TARGETS_V1 and MYC_TARGETS_V2, were found to be enriched in the high TCM group (Figure 10B).Further analysis of the model genes within TCM across pan-cancer settings revealed that TOMM40, NNT5DC2, NNME, KPNA2, FFOXM1 and AP2S1 were predominantly overexpressed in tumour tissues, while TYRP were highly expressed in normal tissues (Figure 10C).Further analysis indicated a significant positive correlation between model genes and TCM scores (Figure 11A).8][29] To discern the molecular characteristics distinguishing different prognostic subtypes and enhance clinical applicability, our research incorporated five multi-center SKCM cohorts, designating TCGA as the training cohort and four additional datasets as validation cohorts.We then employed a combination of 10 machine learning algorithms to select the optimal TCM, aiming to surmount the biases inherent in algorithm selection.In the current landscape where artificial intelligence and vast biological datasets deeply intersect, overfitting presents a critical concern in model construction 30  Subsequently, we analysed the immune-related differences between high and low TCM groups.In the high TCM group, we observed decreased infiltration of various immune cells, indicative of a 'cold' tumour phenotype. 31Conversely, the low TCM group showed abundant immune cell infiltration, suggesting robust anti-tumour immunity. 32Additionally, our literature review identified TOMM40 as an understudied gene in SKCM, showing a significant positive correlation with TCM (cor = 0.77, p < 0.01).Our experiments validated TOMM40's role as an oncogene, enhancing the proliferation, invasion and migration of SKCM cells, potentially making it a key target for SKCM treatment.
Our study offers several novel aspects compared to earlier research.First, we focused on SKCM's pronounced heterogeneity, enabling more precise patient stratification and treatment.
Second, our model, selected based on multiple cohorts, enhances stability and prognostic value.Third, by integrating data from five multi-center cohorts and using 10 widely adopted machine learning algorithms, we identified the model with the best average C-index to establish TCM, minimizing the risk of overfitting.

ACK N OWLED G EM ENTS
The authors express their gratitude for the provision of data by databases such as TCGA and GEO.Sincere appreciation is extended to the reviewers and editors for their valuable comments.

FU N D I N G I N FO R M ATI O N
This study was supported by the Lianyungang Health Youth Science and Technology.Project (No. QN202201).

CO N FLI C T O F I NTER E S T S TATEM ENT
It is hereby declared by the authors that the research was carried out without the presence of any potential conflict of interest arising from commercial or financial relationships.
we have devised a tumour cell-specific machine learning model (TCM).The TCGA dataset was employed as the training cohort, with 4 GEO datasets utilized for validation purposes.The criterion for model selection was based on the average c-index across the six validation cohorts.Ultimately, the CoxBoost and Ridge algorithms were selected as the optimal composite prognostic model (Figure6A).The TCM score successfully stratified patient prognoses across all 5 cohorts (Figure6B-F), with patients in the high TCM group demonstrating poorer survival outcomes compared to those in the low TCM group.

F I G U R E 1
Analysis of cell subtypes and proportions using UMAP clustering.(A) UMAP plot displaying the clustering of 92,521 cells into eight distinct subgroups.(B) Annotation of different clusters using classical markers specific to cell types.(C) UMAP plot showing annotation into five cell types: Tumour cells, NK/T cells, endothelial cells, fibroblasts and myeloid/B cells.(D) Proportional representation of different cell types across various samples.(E) Re-clustering of tumour cells into six distinct clusters.(F) Proportional distribution of the six tumour cell clusters across different samples.F I G U R E 2 Assessment of tumour cells using various bioinformatics tools.(A) Scoring different tumour cells using Cytotrace.(B) Variation in Cytorace scores across different clusters.(C) Evaluation of metabolic heterogeneity in different clusters using scMetabolism.(D) Assessment of pathway activity in different tumour clusters using the ssgsea method.

F I G U R E 3
Pseudotime analysis of tumour cell differentiation and gene expression.(A-D) Pseudotime analysis of differentiation trajectories across six tumour clusters.(E) Trajectories of the three most significantly changing genes over pseudotime.(F) Heatmap showing the trajectories of the top 50 genes with the most significant changes over pseudotime.immune cell infiltration.Employing seven distinct analytical methods, we quantified immune infiltration scores within the TCGA cohort.Our heatmap analysis demonstrated that cohorts with reduced TCM scores exhibited a higher degree of immune cell infiltration (Figure 9A).Subsequent detailed investigations revealed a negative correlation between the TCM scores and matrix scores, immune scores and ESTIMATE scores, indicative of the stromal and immune cell abundance within the TME.Conversely, a positive correlation emerged with tumour purity (Figure 9B-E).Further analyses revealed a significant relationship between TCM and TCIA scores in the context of assessing the TME and predicting immunotherapy responses.The TCIA scores reflect the expression levels of immune checkpoints such as PD-1 and CTLA-4 within the TME.This inverse relationship suggests that tumours with lower TCM scores, which may be less aggressive or have less invasive characteristics, exhibit higher levels of immune checkpoint markers.Consequently, these tumours are potentially more responsive to immunotherapies targeting PD-1/CTLA-4 pathways.(Figure 9F-I).Pathway enrichment analysis revealed a strong association between TCM scores and pathways related to immunotherapy, such as cell cycling and DNA replication, with a significant negative correlation observed for pathways related to the immune cycle as depicted by the TCM scores (Figure S3A).

F I G U R E 4
Assessment of tumour cell cluster abundance and its impact on survival in TCGA-LUAD using ssgsea method.(A-F) Evaluating the abundance of six tumour cell clusters in TCGA-LUAD, followed by an analysis of how the abundance of each cluster affects survival.F I G U R E 5 Analysis of melanoma transcriptomes and survival impact of tumour-specific markers.(A) PCA plot of five melanoma transcriptome datasets after batch effect removal.(B) Univariate Cox regression analysis assessing the impact of tumour-specific markers on survival in TCGA-SCKM, with 37 genes showing prognostic value.(C, D) KEGG and GO enrichment analyses.(E) Localization and copy number variation of 37 prognostic genes across different chromosomes.*p < 0.05, **p < 0.01, ***p < 0.001.F I G U R E 6 Composite machine learning framework and survival analyses across cohorts.(A) A composite machine learning framework was devised, integrating various algorithmic combinations.(B-F) Survival analyses comparing patients with high and low TCM scores across five cohorts.

F I G U R E 7
Comparative prognostic accuracy using the C-index.(A-E) The C-index is utilized as the metric, demonstrating the superior accuracy of TCM scores in prognostication compared to previously published biomarkers.F I G U R E 8 ROC curve analysis and PCA assessment across datasets.(A-E) ROC curves evaluating the accuracy of TCM scores in predicting prognosis.(F-J) PCA assessing the sample distribution across five datasets.F I G U R E 9 Differential immune infiltration analysis.(A) calculation of the disparity in immune cell infiltration between high and low TCM score groups utilizing seven distinct immune infiltration assessment algorithms.(B-E) Evaluation of the correlation between TCM scores and immune scores using the ESTIMATE algorithm.(F-I) TCIA analysis assessing the impact of high and low TCM scores on immune therapy outcomes.F I G U R E 1 0 Pan-cancer comparative analysis using TCM scores.(A) Average levels of TCM scores across a pan-cancer scale.TCGA pancancer gene expression profiles are utilized to calculate TCM scores for different tumour types, displaying the average MOCM scores of patients with various cancer types.(B) Enrichment analysis for hallmark pathways compares tumours with high versus low TCM scores, with enrichment quantified by the GSEA-derived Normalized Enrichment Score (NES).(C) The histogram in the upper panel delineates the count of genes exhibiting significant differential expression, while the heatmap below depicts fold changes and false discovery rates (FDR) for model genes across diverse cancers.Genes significantly upregulated are highlighted in red; downregulated are in green.

3. 10 |
Validating the oncogenic role of TOMM40Among all the genes in the model, TOMM40 exhibits a significant positive correlation with the model score (R = 0.77, p < 0.01, Figure11A), underscoring its prominent impact on the prognosis of SKCM.To elucidate the oncogenic role of TOMM40 in SKCM, we employed siRNA to downregulate the expression of TOMM40 in A357 and WM-115 cell lines (Figure12A).Colony formation assays demonstrated that suppression of TOMM40 markedly inhibited the proliferation and DNA replication capabilities of SKCM cells (Figure12B,C).Wound healing assays were conducted to assess cellular migration capacity.The findings indicated a substantial reduction in the wound closure rate of A357 and WM-115 cells post-TOMM40 knockdown compared to the control group (Figure12D,E).Furthermore, Transwell assays revealed a decrease in the number of cells invading the lower chamber following TOMM40 knockdown (Figure12F-H).Collectively, these outcomes suggest a tumorigenic role for TOMM40 in SKCM cells, corroborating its hazardous contribution in the TCM context.

F I G U R E 11
Model gene analysis and impact on survival.(A) Correlation between model genes and TCM scores.(B) Impact of model genes on survival.F I G U R E 1 2 Validation of TOMM40's oncogenic role in skin cutaneous melanoma (SCKM) through targeted knockdown.(A) Reduction in TOMM40 expression in A375 and WM-115 cells following TOMM40 knockdown, demonstrating effective gene suppression.(B, C) Colony formation assays demonstrating that TOMM40 knockdown significantly inhibits SCKM cell proliferation, showcasing decreased colony numbers in treated cells.(D, E) Wound healing assays to assess the migratory potential of A375 and WM-115 cells post si-TOMM40 transfection, highlighting reduced wound closure.(F-H) Transwell assays to evaluate the migration and invasion capabilities of A375 and WM-115 cells with suppressed TOMM40 expression, indicating decreased cell movement through the membrane.**p < 0.01, ***p < 0.001.
with models performing well in training sets but facing challenges in generalization across validation cohorts.To circumvent the pitfalls of overfitting in the training cohort, we used the average C-index across multiple validation cohorts as a benchmark for model ranking.Our findings revealed that training with CoxBoost and Ridge yielded superior performance in the training set but struggled with generalization across validation sets.Following this rationale, we observed that the carefully curated TCM exhibited robust prognostic value across all cohorts, outperforming other previously published signatures.